Convolutional Neural Network

What is the difference between artificial and convolutional neural networks?

What is the difference between artificial and convolutional neural networks?

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics. The architecture of a ConvNet is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons…
Read More
What is the VGG 19 neural network?

What is the VGG 19 neural network?

VGG 19 is a convolutional neural network architecture that is 19 layers deep. The main purpose for which the VGG net was designed was to win the ILSVRC imagenet competition. Let’s take a brief look at the architecture of VGG19. Input: The VGG-19 takes in an image input size of 224×224.Convolutional Layers: VGG’s convolutional layers leverage a minimal receptive field, i.e., 3×3, the smallest possible size that still captures up/down and left/right. This is followed by a ReLU activation function. ReLU stands for rectified linear unit activation function, it is a piecewise linear function that will output the input if positive otherwise, the output is zero. Stride is…
Read More